Yeast
Yeast 2000; 17: 218±224.
Review Article
The gene guessing game
Ian Dunham*
The Sanger Centre, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK
* Correspondence to:
I. Dunham, The Sanger Centre,
Wellcome Trust Genome
Campus, Hinxton, Cambridge
CB10 1SA, UK.
Abstract
A recent ¯urry of publications and media attention has revived interest in the question of
how many genes exist in the human genome. Here, I review the estimates and use genomic
sequence data from human chromosomes 21 and 22 to establish my own prediction.
Copyright # 2000 John Wiley & Sons, Ltd.
Keywords:
human genome; genes; DNA sequence; chromosome 22; EST
Introduction
How many genes are there in the human genetic
parts list? Forget the 5 year plans [19] and the press
releases [18] Ð the end of the human genome
project will come when we have an accurate answer
to this question. Sure, there will be a few tens or
maybe even hundreds not identi®ed, and perhaps
we won't have the full structure of every gene, but
we will have a pretty good idea of how many genes
there are. And it is not just for the satisfaction of
knowing that we have got to the end, that this
number is important. One of the purposes of
genomics is to allow systematic study of all genes
and their products, and to remove bias inherent in
working with partial datasets. The anticipated postgenomic studies will be compromised if they do not
run with (almost) the full set of human genes. While
monitoring of gene expression using microarrays,
for example, can now sample 10 000 or more human
cDNAs, ideally the experiments should sample all
the genes. The announcement of the completion of
the working draft sequence of the human genome
would suggest we should soon have the answer.
However, a recent cluster of papers [9,17,20] and a
¯urry of speculation [7,21] demonstrates that we still
have a considerable way to go.
What is a gene?
In order to count genes there must be a clear
de®nition of what constitutes a gene. Although the
genes that code for ribosomal, transfer and other
Copyright # 2000 John Wiley & Sons, Ltd.
functional RNAs are essential to the cell, it is the
protein-coding genes that concern most gene counters [4,10]. Mostly this is because the protein-coding
genes must contain the bulk of the functionality and
therefore interest, but in part it is also because it is
thought that they might be easier to count. Of
course, we all know what we mean by a proteincoding gene. However these genes have a number of
methods to increase complexity from a single region
of DNA, including alternative use of promoters,
exons and termination sites. Add to this overlapping
transcription units, somatic recombination in some
of the immune recognition loci, and the existence of
highly similar gene families and pseudogenes, and
de®ning a gene suddenly becomes hard. (I will leave
discussion of the effects of post-translational modi®cations on all this to the proteomics companies.)
Given the lack of consistency of the experimental
estimates below, it is best to keep the de®nition
simple and leave detailed re®nement until later.
Fields et al., [10] discuss these issues and draw the
conclusion that what should be counted are `distinct
transcription units, or parts of transcription units,
that may be translated to generate one or a set of
related amino acid sequences'. In effect, the translation of putative open reading frames in the
transcripts is virtual because it would be impractical
to show that all the genes were actually translated
in some cell and we must rely on the central dogma
for that part. This de®nition seems sensible and it is
suf®ciently linked to the concept that there is a
distinct canonical DNA sequence that encompasses
all the variants of a single gene to enable accurate
counting. All of the experimental approaches used
The gene guessing game
so far tacitly accept that this is what they are
counting.
With this de®nition in mind, why are human
genes hard to count? Complete genome sequences
of Escherichia coli (4300) [5], Saccharomyces cerevisiae (6200) [12], Caenorhabditis elegans (19 000) [23]
and Drosophila (13 600) [1] have given reliable
estimates of gene number in these species. The
problems with human genes really centre on the
greatly expanded size of the genes compared to the
model organisms, particularly in the introns, which
makes modelling of genes direct from genomic
sequence relatively unreliable [8]. Furthermore, at
the current time the working draft sequence adds to
the uncertainty because it consists of 10±20 fragments/100 kb on average, with a relatively high
error rate compared to complete sequence. This
makes genes dif®cult to assemble if they span
sequence contigs and may also prevent discrimination of genes from the frequent pseudogenes.
Con®dent identi®cation of a gene requires supporting evidence from some form of RNA transcript
(cDNA or EST), or from similarity to another gene
at the protein level. Even with complete highaccuracy sequence, there will still be uncertainty as
to whether all genes have been identi®ed, chie¯y
because there always remains the possibility that
there are some genes without signi®cant similarity
to a known gene which are expressed at very low
levels, in obscure tissues or for very short periods.
It is because of these uncertainties that there is
still scope for estimation of gene number by a range
of approaches. Most have chosen to estimate the
complexity of more readily accessible fractions of
the genome which act as surrogates for genes, and
then to scale the estimate to the anticipated
characteristics of the whole genome. The pitfalls of
these methods lie in how well the chosen fraction
corresponds to true genes, and what the true gene
distribution is in the whole genome. In the absence
of complete sequence, this is the only game in town
and it can only end when there is independent
convergence of multiple approaches to a common
estimate.
The methods and the estimates
Early approximations
It is often quoted that there are 50 000±100 000
human genes. It is not clear how this estimate
Copyright # 2000 John Wiley & Sons, Ltd.
219
became ®xed in the literature. Certainly, one source
is from assumptions about the total coding
potential of the genome. If the genome is 3000 Mb
in length and an average gene is 30 kb, then there
is room for 100 000 non-overlapping genes [11]. In
fact, this type of approach is not as naive as it at
®rst appears, providing correction is made for
the fact that not all human genomic DNA is
used for genes. The mean genomic length of
the genes annotated on the complete sequences of
human chromosomes 21 and 22 is at least
28 kb [13,8]. What is missing is that only 32.4% of
the two chromosomal sequences is used for these
genes. Adjusting for this apparent extravagance in
our genomes suggests that there might be 35 000
genes.
A second source for the textbook estimate comes
from the earliest experimental approaches, which
analysed the complexity of mRNA species in
mammalian cells using reassociation kinetics. These
data were summarized by Lewin in Genes IV [16] to
conclude that there were probably 20 000±40 000
human genes and certainly no more than 100 000.
However, by Genes V and VI the ®gure had ®xed on
100 000. What never seems to have been clearly
established in the reassociation studies is how many
mRNA species are unique to a single cell type, and
consequently how far the survey of different cells
must go to effectively sample all genes. Furthermore,
our current knowledge of mRNAs suggests that the
reassociation method is likely to be confounded by
the many alternative transcripts generated from a
single transcription unit.
Since then there have been a number of attempts
to count gene number, resulting in estimates ranging
from 30 000 to more than 120 000. Some estimates
are unpublished and based on proprietary datasets
or techniques [7] and hence are impossible to
critically assess. However, below I review the major
published estimates and revisit calculations based on
the chromosome 21 and 22 genomic sequences.
CpG islands
CpG islands are genomic sequences approximately
1 kb in size which, unlike bulk DNA, are nonmethylated and contain the dinucleotide CpG at the
expected frequency, given their base content. In the
rest of the genome the frequency of CpG dinucleotides is suppressed. CpG islands also have a
signi®cantly higher C+G content than bulk DNA.
Yeast 2000; 17: 218±224.
220
CpG islands are found at the 5k ends of many genes
in mammalian genomes, and hence can be used as
gene markers. Antequera and Bird [3] physically
separated potential CpG island sequences from the
rest of the genome on the basis of their preferential
digestion with HpaII. They then quantitated the
amount of DNA in the CpG island fraction and,
after applying a series of correction factors, calculated that there are 45 000 CpG islands in the
human genome. Since a survey of the gene
sequences then available in the database suggested
that 56% of genes had CpG islands, they extrapolated to 80 000 genes in man. This estimate was
supported by a simultaneous analysis on the mouse
genome, which showed that, although there are
fewer islands in mouse, the fraction of genes with
CpG islands is also lower, so that the method
arrives at 80 000 genes for mouse as well.
The accuracy of this estimation depends in part
on the validity of the correction factors that were
used to compensate for the relative contribution of
tritiated thymidine from non-CpG island and CpG
island DNA to the CpG island-enriched fractions.
Ewing and Green [9] question several aspects of the
calculation, which may lead to over-estimation of
the total number of CpG islands. Furthermore, the
extrapolation from CpG island number to gene
number requires that every island is associated with
a gene. In the chromosome 22 sequence it is clear
that some pseudogenes and repeats have sequences
with CpG island-like properties and that some
genes with CpG islands at the 5k end also contain
other putative islands. So it is possible that this
estimate is too high. However, analysis of the DNA
sequence cannot assay the methylation status of the
putative islands, and it is also plausible that the
experimental separation is selecting almost exclusively for gene-associated CpG islands.
As a footnote to this approach, Incyte Genomics
claimed to have used a CpG island-based sequencing strategy to estimate that 53% of genes have
associated CpG islands and that there are just over
75 000 CpG islands in total in the genome [7]. On
the basis of these ®gures, they predicted that
there may be more than 140 000 genes. Presumably
the main caveats to this estimate are whether the
DNA that is being sequenced is all unmethylated
CpG islands and whether all these sequences are
associated with genes. Anyway, if you remain
unconvinced it is possible to see and hear this
estimate at http : / / www . incyte . com webcast / slides /
Copyright # 2000 John Wiley & Sons, Ltd.
I. Dunham
and http : / / www . incyte . com / webcast / index . html.
(Author's note: Simply must remember to get sound
card installed in PC so that I can hear what Randy
Scott is saying.)
EST clustering
Modern sequencing technologies have allowed an
alternative way to assess the complexity of mRNA
in human cells. There are now large collections of
single-pass sequence reads derived from cDNA
clones from a broad range of human tissues, socalled expressed sequence tags (ESTs) [2,14]. ESTs
may represent different portions of a mRNA,
depending on whether the cDNA library was
primed from polyA or randomly, and whether the
sequence comes from the 5k or 3k end of the clone.
Furthermore, in the efforts to obtain sequences of
mRNAs expressed at low levels there is redundant
sampling of the same mRNAs within the EST
collections. Therefore considerable effort has been
spent developing computational methods to assign
ESTs into clusters that might reconstitute the single
mRNA species. Assessment of these clusters relative
to a representative sample of genes allows a simple
extrapolation to the total number of genes in the
genome [24]. Three papers have applied this method
to human ESTs and genes [9,10,17]. Although
Fields et al.. [10] and Ewing and Green [9] each
describe the logic slightly differently, the underlying
calculation is broadly the same (Figure 1). Liang
et al.. [17] adopted a different approach to the
calculation, which necessitated estimating how
many EST clusters belong to the same gene.
Fields et al. [10] found ESTs matching 1877 of
3483 unique coding regions, leaving 40 077 novel
EST clusters, and estimated between 60 000 and
70 000 genes after correction for presumed redundancy. However, included in the EST clusters were
singleton ESTs, which may have in¯ated the
estimate. There is clear evidence that EST collections contain a number of contaminants, including
genomic DNA and partially spliced forms [17] as
well as less accurate sequences which may not join
clusters or match the test set of genes.
Both Liang et al. [17] and Ewing and Green [9]
carefully removed artefactual and contaminating
sequences from sets of over 1 million ESTs and
clustered them. They each matched their clustered
ESTs with either a set of human transcript
sequences or the chromosome 22 genomic sequence
Yeast 2000; 17: 218±224.
The gene guessing game
221
Comparative sequence analysis
Figure . Estimation of gene number from two sets of gene
sequences. To derive the total number of genes in the
genome (G), take two independently derived sets of gene
sequences. The ®rst is a set of N1 essentially full-length
sequences of an unbiased representative sample of the genes
in the genome. The second set consists of N2 sequences
representing genes that may be redundant and incomplete
and need not be unbiased (i.e. the EST clusters). The
sequence quality of the second set must be suf®cient to
reliably identify the matches to the sequences in the ®rst set.
The ®rst set of genes represents some fraction, f, of the total
number of genes given by f=N1/G, and therefore should also
match a corresponding fraction of the second set of
sequences, f N2. The experimentally determined number of
sequences in the second set that match the ®rst is M2, so
that M2=f N2, and f=M2/N2=N1/G. Therefore the total
number of genes is given by N1N2/M2
to obtain estimates of 120 000 and 35 000 respectively. Why the huge difference? The discrepancy
may lie in the details. The fraction of matches of the
transcript test set to the clusters is different between
the groups. Ewing and Green found 6169 of
their 7662 mRNAs (0.805) matched EST clusters,
whereas Liang et al.found 10 224 out of 18 665
(0.548) matching clusters. It is possible that there
may be bias towards highly expressed genes in the
smaller set, or the presence of transcripts which are
not full-length and therefore do not match 3k ESTs
in the larger set, which might account for the
difference. It is also possible that Liang et al.
underestimated the redundancy of the EST collections because they did not compensate for the
existence of EST clusters that represent the same
gene but do not overlap. There are other differences
too, but clearly it would be helpful to resolve
the discrepancies by an exchange of data and
methodologies.
Copyright # 2000 John Wiley & Sons, Ltd.
Comparative sequence analysis across species offers
an alternative approach. Roest Crollius et al. [20]
used sequence representing one-third of the genome
of the puffer®sh, Tetraodon nigroviridis, to develop
a similarity search tool to identify human genes.
They optimized the conditions of their similarity
search to detect sequence alignments between
homologous pairs of puffer®sh and human genes,
and then applied the optimized search to compare
at the translated level the T. nigroviridis genome
sequence to test sets of human genes and cDNA
sequences. This calibration demonstrated that
65±70% of test genes are detected through exon
matches under conditions where no alignments fall
in introns and that there are between 2.58 and 3.18
overlapping alignments of T. nigroviridis sequence
per human gene. Applying the method to the
annotated genes and pseudogenes in the human
chromosome 22 sequence suggested that there were
approximately 30 previously unfound genes, and
that the sequence contains no more than 600 genes.
89% of the alignments within the chromosome 22
annotation were in genes while the remainder
matched pseudogenes. To get to the estimate of
the number of human genes, they compared the T.
nigroviridis sequence to 42.4% of the human
genome to identify 42 066 overlapping alignments.
Extrapolated to the whole genome at 2.58±3.18
alignments per gene and 89% genes, this gave an
estimate of 28 000±34 000 genes.
Assuming that the test sets of genes used to
calibrate this method are representative, comparative sequencing of the puffer®sh clearly gives a
powerful tool for identi®cation of approximately
two-thirds of human genes. However, as expected,
it cannot provide full gene structures as not all
exons are covered. The basis for detection is
probably through conserved protein domains
rather than by identi®cation of true orthologues,
and hence it remains possible that with more T.
nigroviridis sequence the one-third of human genes
that could not be found will become accessible. On
the other hand, there may be human genes which
have little or no protein similarity with T. nigroviridis sequence and which can never be found.
Ab initio gene prediction
Accurate identi®cation of genes from the working
draft genome sequence using the current ab initio
Yeast 2000; 17: 218±224.
222
gene prediction methods is hampered by both the
nature of the working draft sequence and the falsepositive rates associated with these computational
approaches. In order to increase accuracy, it is
possible to incorporate requirements for con®rmation of gene predictions using similarity matches to
proteins, ESTs or complete cDNAs [15]. The
Ensembl project [22] uses this approach to provide
automated annotation of the working draft
sequence. On the basis of the con®rmed genes
found in the working draft sequence in June 2000,
the Ensembl project estimated that there are 38 000
genes in the genome.
In the private sector, DoubleTwist (http://
www.doubletwist.com) announced in May 2000
that they had used a similar approach of combining
gene predictions with similarity searches to identify
65 000 `high con®dence' genes and 40 000 other
potential genes. These details were made available
through the transcript of a press conference
available
at
http://www.doubletwist.com/info/
pressarticle.jsp?id=art122, but are not published.
Hence, it is dif®cult to be sure about the reasons for
the difference seen between the Ensembl and
DoubleTwist estimates, which ostensibly involve
similar methods. One possibility is that the higher
®gure represents failure to collapse predictions
representing fragments of a gene present in different
working draft contigs into a single gene model.
Annotation of ®nished genomic sequence
The recent completion of the genomic sequence of
the two smallest human autosomes offered the
opportunity to assess gene content from another
perspective [8,13]. Both groups provided detailed
annotation of con®rmed gene structures based on
similarity searches to protein, EST and cDNA as
well as ab initio gene prediction, and extrapolated to
estimates of 40 000±45 000 genes in the genome. At
the time of publication, for the chromosome 22
annotation there was some uncertainty about how
many of the ab initio predictions based on Genscan
would prove to be real genes, and many of the
recent studies above have used the chromosome 22
data to support their observations that there are
either many more genes on chromosome 22 ([17],
Double Twist) or that the annotation was about
right [9,20]. Since that time my group has greatly
extended the initial annotation of chromosome 22,
Copyright # 2000 John Wiley & Sons, Ltd.
I. Dunham
making use of new data in the sequence databases,
sequence matches with T. nigroviridis [20] and
additional screening of cDNA libraries and ®rstround synthesis cDNA (J. Collins, M. Goward,
L. Smink, D. Beare and I. Dunham, unpublished
data). From this work we have been able to
annotate only 17 genes that were previously
unidenti®ed, and have demonstrated that some of
the previous annotations represented multiple fragments of single genes. Furthermore, we have been
able to extend many of the annotations so that an
additional 440 exons (12%) have been included.
Thus there are now 551 genes and 141 pseudogenes
annotated on chromosome 22, excluding the variable gene segments of the immunoglobulin-l locus.
With these updates in mind, I have recalculated
estimates of total gene number based on the
chromosome 21 and 22 data individually and
together (Table 1). In these calculations I treat
uncon®rmed gene predictions separately from annotations with similarity support. I also perform the
calculations with and without compensation for the
relative gene density of the two chromosomes.
Taking the data for the two chromosomes together,
these calculations support a ®gure of 30 000±39 000
genes in the genome. This estimate assumes that the
combination of chromosome 21 and 22 is representative of the rest of the genome. Exactly how
representative they really are remains to be seen.
Although these chromosomes are gene-poor and
gene-rich, respectively, they do not represent the
extremes [6]. Chromosomes 17 and 19 are thought
to be more gene-rich than chromosome 22, while
chromosomes 4, 13, 18, and X may all be more
gene-poor than chromosome 21.
Conclusion
The most recent estimates of the total gene number
for man split into two camps. In the ®rst camp,
which I call the `gene-in¯ators', are estimates of
100 000 and above. In the second camp (the `genede¯ators') are estimates below 40 000. Is it possible
that simply doubling the number of genes compared
to Drosophila or nematode can account for the
additional brain and motor function on which we
pride ourselves? On other hand, would it really take
®ve times as many genes, given that great complexity could be generated by alternative splicing and
post-translational modi®cation? My assessment is
Yeast 2000; 17: 218±224.
The gene guessing game
223
Table 1. Estimates of total human gene number from annotated genes on chromosome 21 and 22 genomic
sequences
Dataset
Genomic
size (bp)
Genome
fraction1
Annotated
genes2
Chr 22
33 400 000
0.0111
551
551
551
551
Chr 21
Chr 21+22
33 500 000
66 900 000
0.0112
0.0223
193
193
744
744
Annotated genes
and predictions3
651
651
651
651
225
225
876
876
Estimated gene
number4
Calculation4
49 500
36 000
40 500
28 000
58 500
42 500
47 500
33 000
17 000
21 000
20 000
24 500
33 500
30 500
39 000
35 500
a
b
c
d
a
b
c
d
a
b
a
b
a
b
a
b
1
A genome size of 3 Gb is assumed to calculate the genome fraction.
The annotated genes are either the current data for chromosome 22 or the reported genes supported by expression data for chromosome 21
[13] i.e. genes identi®ed by ab initio prediction have been removed.
3
For chromosome 22 the estimated 100 predicted gene structures which might also be genes [8] is added to the current annotated set. For
chromosome 21 the supported genes plus predictions are used as described by Hattori et al.
4
For calculations a, b and c total gene number is extrapolated from the number of genes annotated in the chromosome sequence to the whole
genome based on the fraction of the genome represented by the chromosome sequence. The calculations are either (a) uncorrected for relative
gene density on the chromosome or corrected using the factors provided by (b) Deloukas et al. [6], or (c) Liang et al. [17]. In calculation (d) the
relative number of total EST clusters that match chromosome 22 sequence as given in Table 1 of Ewing and Green [9] i.e. 848/43 278 is used to
extrapolate from the number of genes annotated in the sequence to the whole genome. This estimate is independent of the genome fraction
represented by the chromosome 22 sequence. All total gene numbers are rounded to the nearest 500 genes.
2
that the larger estimates are exaggerated by the
complexities of mRNA splicing and termination,
the problems of collapsing multiple EST clusters
into single genes, our imperfect knowledge of CpG
islands, and the existence of many pseudogenes.
Extended annotation of the chromosome 22
sequence has not provided any evidence that there
are two or three times as many genes still to be
found. In fact, very few new genes are being found
and other annotations are being fused together as
more complete transcript information is obtained.
Unless there are many very low-copy or highly
tissue-speci®c transcripts that are not represented in
the public domain databases, I really cannot see
from where 100 000 genes would come. As the
genome sequence is scheduled to be completed over
the next 3 years, surely we should soon have a
satisfactory answer. In the meantime, if you happen
to be in Cold Spring Harbor in the next year, put $5
on there being 40 000 genes for me [21].
Copyright # 2000 John Wiley & Sons, Ltd.
Acknowledgements
The author would like to thank John Collins, Charlotte Cole,
Andy Mungall, David Bentley and Phil Green for their
comments on the manuscript and the Wellcome Trust for
®nancial support.
References
1. Adams MD, Celniker SE, Holt RA, et al. 2000. The genome
sequence of Drosophila melanogaster. Science 287:
2185±2195.
2. Adams MD, Kelley JM, Gocayne JD, et al. 1991. Complementary DNA sequencing: expressed sequence tags and
human genome project. Science 252: 1651±1656.
3. Antequera F, Bird A. 1993. Number of CpG islands and
genes in human and mouse. Proc Natl Acad Sci U S A 90:
11995±11999.
4. Birney E. Gene Sweepstake: http://www.ensembl.org/
genesweep.html
5. Blattner FR, Plunkett G 3rd, Bloch CA, et al. 1997. The
complete genome sequence of Escherichia coli K-12. Science
277: 1453±1474.
Yeast 2000; 17: 218±224.
224
I. Dunham
6. Deloukas P, Schuler GD, Gyapay G, et al. 1998. A physical
map of 30 000 human genes. Science 282: 744±746.
7. Dickson D. 1999. Gene estimate rises as US and UK discuss
freedom of access. Nature 401: 311.
8. Dunham I, Hunt AR, Collins JE, et al. 1999. The DNA
sequence of human chromosome 22. Nature 402: 489±495.
9. Ewing B, Green P. 2000. Analysis of expressed sequence
tags indicates 35,000 human genes. Nature Genet 25:
232±234.
10. Fields C, Adams MD, White O, Venter JC. 1994. How many
genes in the human genome? Nature Genet 7: 345±346.
11. Gilbert W. 1992. The Code of Codes. Harvard University
Press: Cambridge, MA; 83±97.
12. Goffeau A, et al. 1997. The Yeast Genome Directory. Nature
387: 1±105.
13. Hattori M, Fujiyama A, Taylor TD, et al. 2000. The DNA
sequence of human chromosome 21. Nature 405: 311±319.
14. Hillier LD, Lennon G, Becker M et al. 1996. Generation and
analysis of 280 000 human expressed sequence tags. Genome
Res 6: 807±828.
15. Kulp D, Haussler D, Reese MG, Eeckman FH. 1997.
Integrating database homology in a probabilistic gene
structure model. Pac Symp Biocomput 232±244.
16. Lewin B. Genes IV. Oxford University Press: Oxford;
466±481.
17. Liang F, Holt I, Pertea G, Karamycheva S, Salzberg SL,
Quackenbush J. 2000. Gene index analysis of the human
genome estimates approximately 120 000 genes. Nature Genet
25: 239±240.
18. MacIlwain C. 2000. World leaders heap praise on human
genome landmark. Nature 405: 983±984.
19. Marshall E. 1998. NIH to produce a `working draft' of the
genome by 2001. Science 281: 1774±1775.
20. Roest Crollius H, Jaillon O, Bernot A, et al. 2000. Estimate
of human gene number provided by genome-wide analysis
using Tetraodon nigroviridis DNA sequence. Nature Genet
25: 235±238.
21. Smaglik P. 2000. Researchers take a gamble on the human
genome. Nature 405: 264.
22. The Ensembl Project. 2000. http://www.ensembl.org
23. The Celegans Sequencing Consortium. 1998. Genome
sequence of the nematode C. elegans: a platform for
investigating biology. Science 282: 2012±2018.
24. Waterston R, Martin C, Craxton M, et al. 1992. A survey of
expressed genes in Caenorhabditis elegans. Nature Genet 1:
114±123.
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Volume 2014
Hindawi Publishing Corporation
http://www.hindawi.com
Volume 2014
Molecular Biology
International
Hindawi Publishing Corporation
http://www.hindawi.com
Volume 2014
Advances in
Bioinformatics
Hindawi Publishing Corporation
http://www.hindawi.com
Volume 2014
Journal of
Marine Biology
Volume 2014
Hindawi Publishing Corporation
http://www.hindawi.com
Volume 2014